8.10 Big Woods Subsection of Minnesota & NE Iowa Morainal Section

This regional model report is organized
as described in Sections 8.2.1 through 8.4.1. Refer to these sections of
the report for explanations of the tables, variables, and statistics.

8.10.1 Environmental
Context

Big Woods is a subsection of the
Minnesota and Northeastern Iowa Morainal Section of the Eastern Broadleaf Forest
Province (Figure 8.1) (Minnesota DNR 1998).
It is dominated by one definable landform, a loamy mantled end moraine associated
with the Des Moines Lobe of Late Wisconsin Glaciation, and one historic vegetation
community, the Big Woods, a mesic deciduous forest.

To the northeast, the end moraine
contacts outwash sediments on the Anoka Sand Plain (Figure
8.10.2.a). The Big Woods vegetation (Figure 8.10.1.b) ended at this location, with
oak barrens and aspen/oak communities on the outwash plains. At the southeast
boundary the end moraine within the Big Woods subsection meets a ground moraine
in the adjacent subsection. This boundary marks the southern extent of the deciduous
forest and a transition to prairie and oak barrens. The subsection boundary
roughly corresponds to the transition of deciduous forest to prairie.

The area is characterized by rolling
terrain and scattered lakes of varying sizes. The Minnesota River flows southwest
to northeast through the center and divides the subsection approximately in
half (Figure 8.10.1.a). Climate, topography,
water bodies, and loamy soils (Figure 8.10.2.b)
combine to form the basis for a mesic deciduous forest community now known as
the Big Woods.

In the 19th century,
vegetation was primarily Big Woods (Figure 8.10.1.b) (sugar maple, basswood, and
elm), which requires protection from fire to become established. The area’s
rolling terrain and lakes provided this protection. Wet prairies were interspersed
with forest throughout the landscape. Areas of oak openings and barrens were
located north and south of the Minnesota River and along the eastern boundary
of the subsection in Hennepin and Dakota Counties. A separate area of oak barrens
was recorded in north central Waseca County at the southern boundary of the
subsection. Aspen-oak woodland occurred in Scott, northern Rice, Meeker, and
western Wright counties. Prairie was limited to a narrow band along the south
side of the Minnesota River in Scott County, and to small patches in Sibley,
Le Sueur, and Blue Earth counties. River bottom forest was recorded adjacent
to the Minnesota River and to limited sections of the North and South Fork of
the Crow Wing River.

The site probability model (Figure
8.10.3) contains areas of high and medium site potential concentrated along
the region’s streams and lakes. There are two major water features in this subsection,
the Minnesota River and Lake Minnetonka.

An area of high site potential clearly
delineates the Minnesota River in the center of the region. The zone of high
site potential along the river coincides primarily with alluvium and to a lesser
degree with terrace landforms. At least eight tributaries of the Minnesota River
have high or medium site potential. These tributaries include Carver, Silver,
and Bevens Creeks in Carver County; Sand Creek and Credit River in Scott County;
Buffalo Creek and Rush River in Sibley County; and Forest Prairie and Cherry
Creeks in Le Sueur County. The North Fork of the Crow River in southeastern
Wright County is clearly outlined as an area of high site potential.

Extensive areas of high and medium
site potential are found around Lake Minnetonka in southwestern Hennepin County.
This area encompasses Lake Minnetonka and nearby lakes (e.g. Lake Waconia).
High site potential also surrounds the lakes and wetlands in north central Hennepin
County. There is another zone of high site potential centered on Pelican Lake
northeast of Buffalo in Wright County.

In the southern portion of the Big
Woods subsection, high and medium site potential zones surround a series of
lakes extending from the Minnesota River near Mankato northeasterly through
Blue Earth (Eagle Lake, Madison Lake), northwestern Waseca (Lake Elysian), southern
Le Sueur (Lake Washington, Lake Jefferson, and Tetonka Lake), and western Rice
(Shields Lake and Dora Lake) counties. There are slight aberrations, in the
form of north-south trending stripes, in the pattern of site potential in the
southern portion of the subsection. These aberrations are particularly conspicuous
along the transition edges from high to medium and medium to low site potential.
These lines are artifacts of banded digital elevation data (Section
4.5.1.3) and should not be considered predictive features.

8.10.2.2 Evaluation

The site probability model performed
well. It is based on 16 variables (Table 8.10.1) representing
topography, vegetation, and hydrology.

Table 8.10.1.
Site Probability Model, Big Woods Subsection.

Intercept

7.977

ln (nonsites/sites)

0.968588231

Variable

Regression
Coefficient

Probability

Distance to conifers

0.01060460

100.0

Distance to edge of nearest
large wetland

-0.01442645

100.0

Distance to edge of nearest
large lake

-0.02549698

100.0

Distance to edge of nearest
perennial river or stream

-0.01535520

90.9

Distance to nearest lake inlet/outlet

-0.01435861

70.7

Distance to nearest permanent
lake inlet/outlet

0.01041905

42.5

Distance to oak woodland

-0.008558970

99.1

Distance to prairie

0.009153079

88.3

Distance to sugar maple

-0.01408731

85.8

Height above surroundings

0.03988452

100.0

Relative elevation

0.02443549

76

Size of major watershed

-0.0006577646

55.6

Size of nearest permanent
lake

0.0002589899

98.2

Slope

0.2126660

100.0

Surface roughness

-0.06404094

100.0

Vertical distance to water

0.008692292

98.3

In this model, 86.18 percent of
all modeled sites (Table 8.10.2) are in high/medium site
potential areas, which make up only 33.93 percent of the landscape. This produces
a good gain statistic of 0.60629 (Table
8.6.11). The model did not test as well, however. Only 76.81 percent of
new sites were predicted, producing a test gain statistic of 0.55826.

The database included 637 sites
that were not single artifacts. This is nearly twice the average number of sites
for an average Phase 3 region (Table
8.6.11). Two preliminary models using different halves of the known sites
had only 70 percent agreement (70 percent of the cells in the subsection were
classified the same in both models). The Kappa statistic, which is adjusted
for the amount of agreement expected by chance alone for these models, was 0.47874
(Table 8.6.11). The conditional
Kappa statistics (Table 8.10.3) are lowest for the medium
site potential zone. Biased survey distributions (Section
8.10.4) may contribute to the instability of the site probability model.

There are 16 variables involved
in the Big Woods subsection site probability model, seven that are hydrologic
in nature. The remaining variables are divided between topographic (3), vegetation
(4), and soils/landforms (2) categories.

Only two variables have moderate
correlation coefficients (Table 8.10.4) with probability
values for modeled sites- slope (Figure
8.10.4.b) and vertical distance to water (Figures 8.10.6.b).The differences in
means between the modeled sites and random points for these variables is pronounced,
with sites on more sloping ground and higher above water (3.3 degrees and 26.9
feet) compared to random points (2.0 degrees and 15.5 feet, Figures
8.10.9d and 8.10.12d). Three additional variables have
sizable differences in means when modeled sites are compared to random points.
These are distance to nearest large lake (1301 vs. 2324 meters), distance
to hardwoods (59 vs. 111 meters, Figure 8.10.13c), and size of nearest permanent
lake (938 vs. 510 acres, Figure 8.10.12c).

The Mann-Whitney U tests indicate
that of the 16 variables in the model, 11 have values at the 0.05 level of significance
(Table 8.10.4). This means that sites and random points are situated in different
environmental zones, different enough to consider them as being drawn from different
populations.

Generally, sites are closer to a
variety of hydrologic features than are random points. Variables supporting
this conclusion include distance to nearest large lake (Figure 8.10.10d), distance to nearest large
wetland (Figure 8.10.10c), distance to nearest perennial
river or stream (Figure 8.10.11a), distance to nearest lake
inlet/outlet (Figure 8.10.11c), distance to nearest permanent
lake inlet/outlet (Figure 8.10.12a). Clearly large, permanent
water features are also associated with sites. However, why sites tend to be
in smaller watersheds is unclear (Figure 8.10.9c).

Terrain is important, perhaps especially
in combination with proximity to water. While low floodplains may be less habitable
or less likely to preserve surface sites in situ, more steeping sloping ground,
further above water may contain better records of prehistoric habitation. This
observation is supported by the additional model variables height above surroundings (Figure 8.10.9a), relative elevation (Figure 8.10.9b), and surface roughness (Figure 8.10.10a).

Sites in the subsection are closer
to hardwoods (Figure 8.10.13c), sugar maple(Figure 8.10.14b), oak woodland (Figure 8.10.14c), and prairie (Figure 8.10.13d) than are random points. However,
there is little distinction between the locations of modeled sites and random
points for distance to conifers (Figure 8.10.13b). Its contribution to the
model must be in conjunction with other variables. Surprisingly, vegetation
diversity does not enter into this model, despite the large number of vegetation
variables.

Table 8.10.4.
Model Variable Statistics.

Big
Woods

Modeled
Sites

Surveyed
Areas

Modeled
Sites

Sites
in Low Prob.

Neg.
Survey Points

Random
Points

Model
Variable

Coeff.

Sign.

Coeff.

Sign.

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

Height
above surroundings

-

-

0.412

0

18.16

16.72

7.71

6.05

10.69

9.5

9.29

10.2

Relative
elevation

-

-

0.359

0

22.31

17.25

11.59

6.66

14.76

10.14

13.33

11.31

Size
of major watershed

0.016

0.075

0.063

0.748

1468.73

366.92

1490.85

327.6

1525.86

325.77

1516.2

334.11

Slope

0.423

0

-

-

3.33

3.14

1.67

1.23

2.23

1.86

1.98

2.09

Surface
roughness

0.243

0.018

-0.067

0

161.75

17.29

158.65

9.82

157.08

12.91

160.4

13.94

Direction
to nearest water or wetland (sine)

-0.117

0.005

-

-

0.16

0.74

0.33

0.73

0.14

0.73

0.23

0.75

Distance
to edge of nearest large wetland

-

-

-0.12

0

35.45

19.91

41.09

24.35

39.21

19.67

40.85

19.55

Distance
to edge of nearest large lake

-0.292

0

-

-

36.07

30.12

54.16

27.11

44.84

23.48

48.21

27.34

Distance
to edge of nearest perennial river or stream

-

-

-0.242

0

35.84

21.23

44.3

22.32

37.63

19.9

42.53

20.46

Distance
to nearest permanent wetland inlet/outlet

-0.142

0

-

-

195.44

61.73

210.74

57.07

185.69

56.91

206.26

52.2

Distance
to nearest lake inlet/outlet

-0.27

0

-0.25

0

38.39

15.61

49.26

18.11

42.34

18.06

46.16

17.14

Distance
to nearest lake, wetland, organic soil, or stream

-

-

-0.227

0

7.97

5.53

9.17

6.21

7.75

5.74

9.28

6.89

Distance
to nearest permanent lake inlet/outlet

-0.187

0

-

-

56.58

24.33

67.54

22.43

58.3

24.7

65.02

23.75

Size
of nearest lake

-

-

0.067

0.094

846.25

1095.65

273.1

340.16

438.39

687.09

491.45

751.56

Size
of nearest permanent lake

0.26

0

-

-

938.17

1205.99

295.89

366.89

487.73

786.35

510.52

786.15

Vertical
distance to water

0.422

0

0.238

0

26.92

34.07

13.24

22.02

17.09

24.7

15.54

23.7

Distance
to aspen-birch

-

-

-0.228

0

324.58

53.77

319.01

57.94

305.11

45.62

325.45

52.56

Distance
to conifers

-

-

-0.269

0

288.76

49.75

284.43

52.18

271.61

42.97

288.82

45.9

Distance
to hardwoods

-0.042

0.084

-

-

7.68

13.71

9.29

16.37

9.97

16.18

10.55

18.78

Distance
to prairie

-0.059

0.211

0.097

0.103

84.97

41.8

76.14

39.39

85.57

40.01

83.66

37.58

Distance
to river bottom forest

-

-

-0.071

0

94.93

52.95

102.87

49.62

96.49

41.02

105.72

46.64

Distance
to sugar maple

-0.067

0

-0.066

0.015

35.35

14.5

40.55

16.26

38.19

16.46

40.29

20.88

Distance
to oak woodland

-0.111

0.074

-0.181

0

73.98

41.45

70.49

36.25

68.84

45.48

76.88

43.06

On
alluvium

0.169

0

0.049

0.08

0.06

0.25

0

0

0.02

0.14

0.02

0.15

On
river terraces

0.036

0.171

-0.015

0.864

0.05

0.22

0.04

0.19

0.03

0.18

0.04

0.19

Frequency
counts

637

1993

637

83

1291

1678

See Interpretation section for explanation of the statistics:
Coeff. = correlation coefficient
Sign. = significance of the Mann-Whitney U test
S.D. = standard deviation
Distances and areas
are expressed in square roots of meters. Square the values in the table to get
actual values.

8.10.2.3.2 Relationships between
Variable Pairs

Table 8.10.5,
which is a correlation matrix of all possible combinations of variables used
in the site model, is useful in exploring the interrelationships between variables.
For example, distance to prairie and distance to oak woodland have a high coefficient of 0.71, indicating that as distance to prairie increases
(or decreases), it also increases (or decreases) to oak woodlands. Prairie is
the least prevalent vegetation community in this subsection. Where it is found,
it is usually adjacent to oak woodland or aspen-oak woodland. The primary relationships
between these communities is their tolerance to fire. This sets them apart from
the dominant Big Woods vegetation in both composition and distribution within
the landscape. Distance to the nearest large lake has a high negative
correlation (-0.68) with size of permanent lake which means that as lake
size increases, distance decreases, or vice versa. In other words, sites in
the Big Woods tend to be concentrated around large lakes. Distance to nearest
permanent wetland inlet/outlet is also inversely related to size of major
watershed (-0.74). This makes sense since larger watersheds in the subsection
(i.e. Minnesota River) also have a more developed drainage system composed of
a more dense system of stream-drained wetlands. Finally, the moderate positive
correlation between slope and surface roughness (0.57) (Figure
8.10.5.a) is due to the fact that surface roughness is a derivative of topographic
variables (elevation, slope, and relative elevation).

Table 8.10.5.
Variable Correlation Matrix.

Big
Woods

Size
of major watershed

Slope

Surface
roughness

Direction
to nearest water or wetland (sine)

Distance
to edge of nearest large lake

Distance
to nearest permanent wetland inlet/outlet

Distance
to nearest lake inlet/outlet

Distance
to nearest permanent lake inlet/outlet

Size
of nearest permanent lake

Size
of major watershed

1

Slope

0.09

1

Surface
roughness

-0.25

0.57

1

Direction
to nearest water or wetland (sine)

-0.04

-0.12

-0.1

1

Distance
to edge of nearest large lake

0.1

0.04

-0.22

-0.02

1

Distance
to nearest permanent wetland inlet/outlet

-0.74

-0.06

0.31

0

0.06

1

Distance
to nearest lake inlet/outlet

-0.01

-0.06

0.03

0

0.13

0.06

1

Distance
to nearest permanent lake inlet/outlet

-0.24

-0.11

0.23

0

0.04

0.36

0.44

1

Size
of nearest permanent lake

-0.11

-0.09

0.23

-0.01

-0.68

0.11

0.02

0.14

1

Vertical
distance to water

0.03

0.29

0.35

-0.26

0.07

-0.02

-0.04

-0.06

0.06

Distance
to hardwoods

0.14

-0.11

-0.2

0.04

0.04

-0.18

-0.05

-0.09

0

Distance
to prairie

-0.17

-0.05

0.22

0.02

-0.41

-0.04

-0.03

-0.02

0.24

Distance
to sugar maple

0.06

-0.06

-0.18

0

0.11

-0.08

-0.08

0.01

-0.06

Distance
to oak woodland

-0.42

-0.05

0.29

0.01

-0.27

0.41

-0.01

0.05

0.22

On
alluvium

0.13

0.01

-0.24

0.03

0.3

-0.08

0.02

-0.06

-0.16

On
river terraces

0.16

0.08

-0.21

-0.06

0.29

-0.06

0.02

-0.02

-0.21

Vertical
distance to water

Distance
to hardwoods

Distance
to prairie

Distance
to sugar maple

Distance
to oak woodland

On
alluvium

On
river terraces

Size
of major watershed

Slope

Surface
roughness

Direction
to nearest water or wetland (sine)

Distance
to edge of nearest large lake

Distance
to nearest permanent wetland inlet/outlet

Distance
to nearest lake inlet/outlet

Distance
to nearest permanent lake inlet/outlet

Size
of nearest permanent lake

Vertical
distance to water

1

Distance
to hardwoods

-0.1

1

Distance
to prairie

-0.05

-0.23

1

Distance
to sugar maple

-0.07

0.38

-0.28

1

Distance
to oak woodland

0.01

-0.37

0.71

-0.22

1

On
alluvium

0.04

0.13

-0.35

0.12

-0.2

1

On
river terraces

0.06

0.04

-0.3

0.11

-0.16

-0.06

1

Refer
to "Relationships between variable pairs" for interpretation of the
table values.

8.10.2.3.3 Sites in Low Probability
Areas

The site model for this subsection
places sites located in low probability areas in environments most similar to
what is expected by random chance. Of the 16 variables, the mean values of sites
in low potential areas are nearest random points on 12, including slope (1.7 vs. 2.0 degrees), vertical distance to water (13.2 vs. 15.4 feet), distance to nearest large lake (2933 vs. 2324 meters), and size of
nearest permanent lake (296 vs. 510 acres). On two of the variables, size
of major watershed and distance to hardwoods, the sites in low potential
areas are between the random points and modeled site means.

Of these 83 sites, 50 are lithic
scatters and another 15 are artifact scatters. If all site types are equally
well predicted by the model, 13.03 percent of each site type should be in the
low probability areas. Artifact scatters approach this proportion, with 11.19
percent of all artifact scatters in low probability areas. Earthworks/mounds
are underrepresented in low probability areas (only 6.81 percent). Lithic scatters
(17.36 percent), base camps (28.57 percent) and burial locations (37.5 percent)
are underrepresented, indicating that the model does a relatively poor job predicting
such sites.

8.10.2.3.4 Relationships between
Cultural Context, Descriptive, or Settlement Variables and Site Potential

Table 8.10.6presents the results of four bivariate relationships comparing four dichotomous
variables with three areas of site potential. Percentages and frequencies of
all sites are also included as a comparative baseline. Referring to the table,
it is apparent that sites containing Archaic components are over represented
in the low and medium site potential areas compared to those lacking Archaic
components (31.3 percent and 25.0 percent vs. 7.4 percent and 14.1 percent).
The results of the chi-square test indicate that this relationship is significant
at the 0.05 level, meaning it deviates from that expected by chance or independence.
However, the small sample of Archaic sites (16) makes this conclusion tentative.
Aceramic sites also occur more frequently in the low and medium site potential
areas compared to those containing pottery (17.7 percent and 19.9 percent vs.
7.5 percent and 15.5 percent). Although pre-Woodland sites may account for a
portion of this relationship, it may be more likely that the aceramic sites
are dominated by short-term activities (e.g. lithic scatters) of all periods.
There is also a tendency for multiple component sites to be in low and medium
site potential areas compared to sites with single components (28.6 percent
and 28.6 percent vs. 7.7 percent and 14.0 percent). Once again, the low number
of multiple component sites indicates that this relationship is tentative in
nature. There is no significant difference in the distribution of mound and
non-mound sites.

Table 8.10.6.
Summary of Bivariate Relationships between Four Dichotomous Archaeological Variables
and Three Areas of Site Potential, Big Woods (Site Probability Model).

The survey probability model for
the Big Woods (Figure 8.10.7) indicates
that surveys have been concentrated in Carver, Hennepin, and eastern Wright
Counties. More than 75 percent of the surveys have occurred in these counties,
which make up approximately half the area of the subsection. Many surveys in
these three counties are randomly distributed along highways with a frequency
of 2-3 surveys per 1 linear km. Some linear surveys follow streams or are concentrated
near lakes. Significant random element and frequent surveys distribution in
this part of Big Wood subsection were sufficiently effective to classify most
of the area as high and medium potential zones.

Outside of these areas, surveys
are have been adjacent to the Minnesota River and other bodies of water. Only
short linear surveys, for example, along the State Highway 22 between Le Sueur
and Blue Earth counties are recorded. The areas of high and medium survey potential
follow many of the same water features as in the site model. In the south half
of the subsection the survey model displays a characteristic pattern of high
and medium survey potential zones around water features. Specifically, these
zones are located north from Mankato along the Minnesota River, along the southern
subsection border around Elysian Lake and Cannon Lakes, and in lighter concentrations
in Rice county around its many lakes (i.e. Fox, Union).

Major portions of Blue Earth, Le
Sueur, and Scott counties remain undersurveyed. The average frequency of surveys
in Carver county is 0.48 surveys per 1 sq. km, while in Le Sueur county it is
only 0.078.

There are similar aberrations in
the survey model as are seen in the site model. These north-south trending stripes
in the pattern of survey potential in the southern portion of the subsection
are artifacts of banded digital elevation data (Section
4.5.1.3) and should not be considered predictive features.

In this model, 84.7 percent of all
sites and negative survey points (Table 8.10.8) are in
high/medium survey potential areas, which make up 53.32 percent of landscape
(Table 8.10.8). This produces a very weak gain statistic
of 0.37048 (Table 8.6.14), which is acceptable
for a survey probability model. This model was able to predict surveyed places
with much less precision than the site probability model was able to predict
sites, indicating that sites are confined to only a portion of the total area
surveyed. However, the model predicted surveyed places better than by chance.
Although frequencies of 0.25 surveyed points per square km (Table
8.6.14) and 0.087 known sites per square km (Table
8.6.11) are rather high for Minnesota, this model illustrates the non-random
pattern of previous surveys. Further surveys in the southern and northwestern
portions of the subsection should improve this.

The number of surveyed places (702
sites of all kinds and 1291 negative survey points) in the Big Woods subsection
is high relative to other parts of the state (Table
8.6.14). Nevertheless, two preliminary survey probability models had only
63 percent agreement. The Kappa statistic for these models was 0.42355. Such
poor performance may be attributable to the biased distribution of surveys.
The conditional Kappa statistics (Table 8.10.9) are lowest
for the medium site potential zone, which is less than 20 percent of the subsection
area. Conducting surveys more randomly throughout the undersurveyed areas should
increase the stability of the model.

Eighteen variables are included
in the survey probability model, nine of which are also in the site probability
model. Nearly all of the correlation coefficients of survey model variables
are low (Table 8.10.4); only two, height above surroundings (Figure 8.10.9.a) and relative elevation (Figure 8.10.9.b), have correlations with surveyed
places of 0.3 and above. This indicates that surveyed places tend to have greater
relief and are higher than their surroundings compared to areas that have been
undersurveyed. However, this appears to be from the influence of sites in the
database of surveyed places. Negative survey points are more similar in these
characteristics to random points.

There are only five variables in
this model that arenot significant at the 0.05
level, as measured by the Mann-Whitney U test (Table 8.10.4).
This implies that archaeological surveys in the Big Woods are nonrandomly distributed
on a series of environmental characteristics. The pattern is apparent from an
examination of the means (Table 8.10.4) and histograms
of these variables for the populations of negative survey points, modeled sites,
and random points. These are discussed in Section 8.10.2.3.1 and, for variables appearing only in the survey probability model, below.

In some respects, the population
of negative survey points is quite similar to that of known sites when compared
to random points. They are closer to perennial rivers and streams (Figure 8.10.11a), to lake, wetlands, organic
soils, and streams ( Figure 8.10.11d), and to river bottom forest
(Figure 8.10.14a) than are random points.

Surveyed places and random points
tend to be farther from large lakes than are modeled sites (Figures 8.10.10d, 8.10.12b and 8.10.12c). For the variables distance to
aspen-birch (Figure 8.10.13a), and distance to conifers (Figure 8.10.13b), there is little distinction
between modeled sites, negative survey points, and random points.

8.10.4 Survey
Implementation Model

The survey implementation model
for the Big Woods indicates that 37.61 percent of the land area is categorized
as unknown because of inadequate survey (Table 8.10.10 and Figure 8.10.8). As indicated by the survey
implementation model, these areas are in the southern and northwestern portions
of the subsection. The proportion of the unknown area occupied by wetlands,
pavement, and other unsurveyable land covers has not been calculated. High probability
areas are concentrated strongly around water bodies, including significant areas
of suspected and possibly high probability in places unlike those that have
been well surveyed. Low probability areas are primarily between the high probability
and the unknown areas. With the unknown area removed, the low and possibly low
probability areas occupy 25.41 percent of the landscape. This emphasizes the
need for more surveys in the unknown areas to improve the models.

River valley floors and terraces
in the Big Woods subsection have the potential to contain deeply buried archaeological
sites. Mn/Model landscape suitability models were created for the Minnesota
River Valley through this subsection (Section
12.3). In addition, a very small portion of the Mississippi River Valley
between St. Paul and St. Cloud coincides with the Big Woods subsection. That
stretch of the river is now being mapped and modeled for landscape suitability
by MnDOT. These models should be consulted for information regarding the geomorphic
potential for both surface and subsurface archaeological sites.